Generate FAIR Literature Surveys with Scholarly Knowledge Graphs

TL;DR

This paper introduces a system leveraging scholarly knowledge graphs to facilitate literature reviews by comparing research contributions, aligning descriptions, and enabling FAIR-compliant publication of surveys, thus streamlining research synthesis.

Contribution

It presents a novel methodology and system for comparing scholarly literature using knowledge graphs, including tasks like finding, aligning, visualizing, and publishing research contributions.

Findings

Effective comparison of research contributions demonstrated

System supports FAIR-compliant publication of surveys

Evaluation shows promising results using published review data

Abstract

Reviewing scientific literature is a cumbersome, time consuming but crucial activity in research. Leveraging a scholarly knowledge graph, we present a methodology and a system for comparing scholarly literature, in particular research contributions describing the addressed problem, utilized materials, employed methods and yielded results. The system can be used by researchers to quickly get familiar with existing work in a specific research domain (e.g., a concrete research question or hypothesis). Additionally, it can be used to publish literature surveys following the FAIR Data Principles. The methodology to create a research contribution comparison consists of multiple tasks, specifically: (a) finding similar contributions, (b) aligning contribution descriptions, (c) visualizing and finally (d) publishing the comparison. The methodology is implemented within the Open Research Knowledge Graph (ORKG), a scholarly infrastructure that enables researchers to collaboratively describe, find and compare research contributions. We evaluate the implementation using data extracted from published review articles. The evaluation also addresses the FAIRness of comparisons published with the ORKG.